def _loadh5(self, filename):
"""Load PYMEs semi-custom HDF5 image data format. Offloads all the
hard work to the HDFDataSource class"""
import tables
from PYME.IO.DataSources import HDFDataSource, BGSDataSource
from PYME.IO import tabular
self.dataSource = HDFDataSource.DataSource(filename, None)
#chain on a background subtraction data source, so we can easily do
#background subtraction in the GUI the same way as in the analysis
self.data = BGSDataSource.DataSource(
self.dataSource) #this will get replaced with a wrapped version
if 'MetaData' in self.dataSource.h5File.root: #should be true the whole time
self.mdh = MetaData.TIRFDefault
self.mdh.copyEntriesFrom(
MetaDataHandler.HDFMDHandler(self.dataSource.h5File))
else:
self.mdh = MetaData.TIRFDefault
import wx
wx.MessageBox(
"Carrying on with defaults - no gaurantees it'll work well",
'ERROR: No metadata found in file ...', wx.OK)
print(
"ERROR: No metadata fond in file ... Carrying on with defaults - no gaurantees it'll work well"
)
#attempt to estimate any missing parameters from the data itself
try:
MetaData.fillInBlanks(self.mdh, self.dataSource)
except:
logger.exception('Error attempting to populate missing metadata')
#calculate the name to use when we do batch analysis on this
#from PYME.IO.FileUtils.nameUtils import getRelFilename
self.seriesName = getRelFilename(filename)
#try and find a previously performed analysis
fns = filename.split(os.path.sep)
cand = os.path.sep.join(fns[:-2] + [
'analysis',
] + fns[-2:]) + 'r'
print(cand)
if False: #os.path.exists(cand):
h5Results = tables.open_file(cand)
if 'FitResults' in dir(h5Results.root):
self.fitResults = h5Results.root.FitResults[:]
self.resultsSource = tabular.H5RSource(h5Results)
self.resultsMdh = MetaData.TIRFDefault
self.resultsMdh.copyEntriesFrom(
MetaDataHandler.HDFMDHandler(h5Results))
self.events = self.dataSource.getEvents()
self.mode = 'LM'
def convertFile(inFile, outFile):
ds = tabular.H5RSource(inFile)
nRecords = len(ds[ds.keys()[0]])
of = open(outFile, 'w')
of.write('#' + '\t'.join(['%s' % k for k in ds._keys]) + '\n')
for row in zip(*[ds[k] for k in ds._keys]):
of.write('\t'.join(['%e' % c for c in row]) + '\n')
of.close()
def _ds_from_file(self, filename, **kwargs):
"""
loads a data set from a file
Parameters
----------
filename : str
kwargs : any additional arguments (see OpenFile)
Returns
-------
ds : tabular.TabularBase
the datasource, complete with metadatahandler and events if found.
"""
mdh = MetaDataHandler.NestedClassMDHandler()
events = None
if os.path.splitext(filename)[1] == '.h5r':
import tables
h5f = tables.open_file(filename)
self.filesToClose.append(h5f)
try:
ds = tabular.H5RSource(h5f)
if 'DriftResults' in h5f.root:
driftDS = tabular.H5RDSource(h5f)
self.driftInputMapping = tabular.MappingFilter(driftDS)
#self.dataSources['Fiducials'] = self.driftInputMapping
self.addDataSource('Fiducials', self.driftInputMapping)
if len(ds['x']) == 0:
self.selectDataSource('Fiducials')
except: #fallback to catch series that only have drift data
logger.exception('No fitResults table found')
ds = tabular.H5RDSource(h5f)
self.driftInputMapping = tabular.MappingFilter(ds)
#self.dataSources['Fiducials'] = self.driftInputMapping
self.addDataSource('Fiducials', self.driftInputMapping)
#self.selectDataSource('Fiducials')
# really old files might not have metadata, so test for it before assuming
if 'MetaData' in h5f.root:
mdh = MetaDataHandler.HDFMDHandler(h5f)
if ('Events' in h5f.root) and ('StartTime' in mdh.keys()):
events = h5f.root.Events[:]
elif filename.endswith('.hdf'):
#recipe output - handles generically formatted .h5
import tables
h5f = tables.open_file(filename)
self.filesToClose.append(h5f)
#defer our IO to the recipe IO method - TODO - do this for other file types as well
self.recipe._inject_tables_from_hdf5('', h5f, filename, '.hdf')
for dsname, ds_ in self.dataSources.items():
#loop through tables until we get one which defines x. If no table defines x, take the last table to be added
#TODO make this logic better.
ds = ds_
if 'x' in ds.keys():
# TODO - get rid of some of the grossness here
mdh = getattr(ds, 'mdh', mdh)
events = getattr(ds, 'events', events)
break
elif os.path.splitext(filename)[1] == '.mat': #matlab file
if 'VarName' in kwargs.keys():
#old style matlab import
ds = tabular.MatfileSource(filename, kwargs['FieldNames'],
kwargs['VarName'])
else:
if kwargs.get('Multichannel', False):
ds = tabular.MatfileMultiColumnSource(filename)
else:
ds = tabular.MatfileColumnSource(filename)
# check for column name mapping
field_names = kwargs.get('FieldNames', None)
if field_names:
if kwargs.get('Multichannel', False):
field_names.append(
'probe') # don't forget to copy this field over
ds = tabular.MappingFilter(
ds, **{
new_field: old_field
for new_field, old_field in zip(
field_names, ds.keys())
})
elif os.path.splitext(filename)[1] == '.csv':
#special case for csv files - tell np.loadtxt to use a comma rather than whitespace as a delimeter
if 'SkipRows' in kwargs.keys():
ds = tabular.TextfileSource(filename,
kwargs['FieldNames'],
delimiter=',',
skiprows=kwargs['SkipRows'])
else:
ds = tabular.TextfileSource(filename,
kwargs['FieldNames'],
delimiter=',')
else: #assume it's a tab (or other whitespace) delimited text file
if 'SkipRows' in kwargs.keys():
ds = tabular.TextfileSource(filename,
kwargs['FieldNames'],
skiprows=kwargs['SkipRows'])
else:
ds = tabular.TextfileSource(filename, kwargs['FieldNames'])
# make sure mdh is writable (file-based might not be)
ds.mdh = MetaDataHandler.NestedClassMDHandler(mdToCopy=mdh)
if events is not None:
# only set the .events attribute if we actually have events.
# ensure that events are sorted in increasing time order
ds.events = events[np.argsort(events['Time'])]
return ds
def _ds_from_file(self, filename, **kwargs):
"""
loads a data set from a file
Parameters
----------
filename : str
kwargs : any additional arguments (see OpenFile)
Returns
-------
ds : the dataset
"""
if os.path.splitext(filename)[1] == '.h5r':
import tables
h5f = tables.open_file(filename)
self.filesToClose.append(h5f)
try:
ds = tabular.H5RSource(h5f)
if 'DriftResults' in h5f.root:
driftDS = tabular.H5RDSource(h5f)
self.driftInputMapping = tabular.MappingFilter(driftDS)
#self.dataSources['Fiducials'] = self.driftInputMapping
self.addDataSource('Fiducials', self.driftInputMapping)
if len(ds['x']) == 0:
self.selectDataSource('Fiducials')
except: #fallback to catch series that only have drift data
logger.exception('No fitResults table found')
ds = tabular.H5RDSource(h5f)
self.driftInputMapping = tabular.MappingFilter(ds)
#self.dataSources['Fiducials'] = self.driftInputMapping
self.addDataSource('Fiducials', self.driftInputMapping)
#self.selectDataSource('Fiducials')
#catch really old files which don't have any metadata
if 'MetaData' in h5f.root:
self.mdh.copyEntriesFrom(MetaDataHandler.HDFMDHandler(h5f))
if ('Events' in h5f.root) and ('StartTime' in self.mdh.keys()):
self.events = h5f.root.Events[:]
elif filename.endswith('.hdf'):
#recipe output - handles generically formatted .h5
import tables
h5f = tables.open_file(filename)
self.filesToClose.append(h5f)
for t in h5f.list_nodes('/'):
if isinstance(t, tables.table.Table):
tab = tabular.HDFSource(h5f, t.name)
self.addDataSource(t.name, tab)
if 'EventName' in t.description._v_names: #FIXME - we shouldn't have a special case here
self.events = t[:] # this does not handle multiple events tables per hdf file
if 'MetaData' in h5f.root:
self.mdh.copyEntriesFrom(MetaDataHandler.HDFMDHandler(h5f))
for dsname, ds_ in self.dataSources.items():
#loop through tables until we get one which defines x. If no table defines x, take the last table to be added
#TODO make this logic better.
ds = ds_.resultsSource
if 'x' in ds.keys():
break
elif os.path.splitext(filename)[1] == '.mat': #matlab file
if 'VarName' in kwargs.keys():
#old style matlab import
ds = tabular.MatfileSource(filename, kwargs['FieldNames'], kwargs['VarName'])
else:
ds = tabular.MatfileColumnSource(filename)
elif os.path.splitext(filename)[1] == '.csv':
#special case for csv files - tell np.loadtxt to use a comma rather than whitespace as a delimeter
if 'SkipRows' in kwargs.keys():
ds = tabular.TextfileSource(filename, kwargs['FieldNames'], delimiter=',', skiprows=kwargs['SkipRows'])
else:
ds = tabular.TextfileSource(filename, kwargs['FieldNames'], delimiter=',')
else: #assume it's a tab (or other whitespace) delimited text file
if 'SkipRows' in kwargs.keys():
ds = tabular.TextfileSource(filename, kwargs['FieldNames'], skiprows=kwargs['SkipRows'])
else:
ds = tabular.TextfileSource(filename, kwargs['FieldNames'])
return ds
def loadInput(self, filename, key='input'):
"""Load input data from a file and inject into namespace
Currently only handles images (anything you can open in dh5view). TODO -
extend to other types.
"""
#modify this to allow for different file types - currently only supports images
from PYME.IO import unifiedIO
import os
extension = os.path.splitext(filename)[1]
if extension in ['.h5r', '.h5', '.hdf']:
import tables
from PYME.IO import MetaDataHandler
from PYME.IO import tabular
with unifiedIO.local_or_temp_filename(filename) as fn:
with tables.open_file(fn, mode='r') as h5f:
#make sure our hdf file gets closed
key_prefix = '' if key == 'input' else key + '_'
try:
mdh = MetaDataHandler.NestedClassMDHandler(
MetaDataHandler.HDFMDHandler(h5f))
except tables.FileModeError: # Occurs if no metadata is found, since we opened the table in read-mode
logger.warning(
'No metadata found, proceeding with empty metadata'
)
mdh = MetaDataHandler.NestedClassMDHandler()
for t in h5f.list_nodes('/'):
# FIXME - The following isinstance tests are not very safe (and badly broken in some cases e.g.
# PZF formatted image data, Image data which is not in an EArray, etc ...)
# Note that EArray is only used for streaming data!
# They should ideally be replaced with more comprehensive tests (potentially based on array or dataset
# dimensionality and/or data type) - i.e. duck typing. Our strategy for images in HDF should probably
# also be improved / clarified - can we use hdf attributes to hint at the data intent? How do we support
# > 3D data?
if isinstance(t, tables.VLArray):
from PYME.IO.ragged import RaggedVLArray
rag = RaggedVLArray(
h5f, t.name, copy=True
) #force an in-memory copy so we can close the hdf file properly
rag.mdh = mdh
self.namespace[key_prefix + t.name] = rag
elif isinstance(t, tables.table.Table):
# pipe our table into h5r or hdf source depending on the extension
tab = tabular.H5RSource(
h5f, t.name
) if extension == '.h5r' else tabular.HDFSource(
h5f, t.name)
tab.mdh = mdh
self.namespace[key_prefix + t.name] = tab
elif isinstance(t, tables.EArray):
# load using ImageStack._loadh5, which finds metdata
im = ImageStack(filename=filename, haveGUI=False)
# assume image is the main table in the file and give it the named key
self.namespace[key] = im
elif extension == '.csv':
logger.error('loading .csv not supported yet')
raise NotImplementedError
elif extension in ['.xls', '.xlsx']:
logger.error('loading .xls not supported yet')
raise NotImplementedError
else:
self.namespace[key] = ImageStack(filename=filename, haveGUI=False)
def _inject_tables_from_hdf5(self, key, h5f, filename, extension):
"""
Search through hdf5 file nodes and add them to the recipe namespace
Parameters
----------
key : str
base key name for loaded file components, if key is not the default 'input', each file node will be loaded into
recipe namespace with `key`_`node_name`.
h5f : file
open hdf5 file
filename : str
full filename
extension : str
file extension, used here mainly to toggle which PYME.IO.tabular source is used for table nodes.
"""
import tables
from PYME.IO import MetaDataHandler, tabular
key_prefix = '' if key == 'input' else key + '_'
# Handle a 'MetaData' group as a special case
# TODO - find/implement a more portable way of handling metadata in HDF (e.g. as .json in a blob) so that
# non-python exporters have a chance of adding metadata
if 'MetaData' in h5f.root:
mdh = MetaDataHandler.NestedClassMDHandler(
MetaDataHandler.HDFMDHandler(h5f))
else:
logger.warning('No metadata found, proceeding with empty metadata')
mdh = MetaDataHandler.NestedClassMDHandler()
events = None
# handle an 'Events' table as a special case (so that it can be attached to subsequently loaded tables)
# FIXME - this relies on a special /reserved table name and format and could raise name collision issues
# when importing 3rd party / generic HDF
# FIXME - do we really want to attach events (which will not get propagated through recipe modules)
if ('Events' in h5f.root):
if 'EventName' in h5f.root.Events.description._v_names:
# check that the event table is formatted as we expect
if ('StartTime' in mdh.keys()):
events = h5f.root.Events[:]
else:
logger.warning(
'Acquisition events found in .hdf, but no "StartTime" in metadata'
)
else:
logger.warning(
'Table called "Events" found in .hdf does not match the signature for acquisition events, ignoring'
)
for t in h5f.list_nodes('/'):
# FIXME - The following isinstance tests are not very safe (and badly broken in some cases e.g.
# PZF formatted image data, Image data which is not in an EArray, etc ...)
# Note that EArray is only used for streaming data!
# They should ideally be replaced with more comprehensive tests (potentially based on array or dataset
# dimensionality and/or data type) - i.e. duck typing. Our strategy for images in HDF should probably
# also be improved / clarified - can we use hdf attributes to hint at the data intent? How do we support
# > 3D data?
if getattr(t, 'name', None) == 'Events':
# NB: This assumes we've handled this in the special case earlier, and blocks anything in a 3rd party
# HDF events table from being seen.
# TODO - do we really want to have so much special case stuff in our generic hdf handling? Are we sure
# that events shouldn't be injected into the namespace (given that events do not propagate through recipe modules)?
continue
elif isinstance(t, tables.VLArray):
from PYME.IO.ragged import RaggedVLArray
rag = RaggedVLArray(
h5f, t.name, copy=True
) #force an in-memory copy so we can close the hdf file properly
rag.mdh = mdh
if events is not None:
rag.events = events
self.namespace[key_prefix + t.name] = rag
elif isinstance(t, tables.table.Table):
# pipe our table into h5r or hdf source depending on the extension
tab = tabular.H5RSource(
h5f, t.name) if extension == '.h5r' else tabular.HDFSource(
h5f, t.name)
tab.mdh = mdh
if events is not None:
tab.events = events
self.namespace[key_prefix + t.name] = tab
elif isinstance(t, tables.EArray):
# load using ImageStack._loadh5
# FIXME - ._loadh5 will load events lazily, which isn't great if we got here after
# sending file over clusterIO inside of a context manager -> force it through since we already found it
im = ImageStack(filename=filename,
mdh=mdh,
events=events,
haveGUI=False)
# assume image is the main table in the file and give it the named key
self.namespace[key] = im
def generateThumbnail(inputFile, thumbSize):
f1 = tabular.H5RSource(inputFile)
threeD = False
stack = False
split = False
#print f1.keys()
if 'fitResults_Ag' in f1.keys():
#if we used the splitter set up a mapping so we can filter on total amplitude and ratio
f1_ = tabular.MappingFilter(f1, A='fitResults_Ag + fitResults_Ar', gFrac='fitResults_Ag/(fitResults_Ag + fitResults_Ar)')
#f2 = inpFilt.resultsFilter(f1_, error_x=[0,30], A=[5, 1e5], sig=[100/2.35, 350/2.35])
split = True
else:
f1_ = f1
if 'fitResults_sigma' in f1.keys():
f2 = tabular.ResultsFilter(f1_, error_x=[0, 30], A=[5, 1e5], sig=[100 / 2.35, 350 / 2.35])
else:
f2 = tabular.ResultsFilter(f1_, error_x=[0, 30], A=[5, 1e5])
if 'fitResults_z0' in f1_.keys():
threeD = True
if 'Events' in dir(f1.h5f.root):
events = f1.h5f.root.Events[:]
evKeyNames = set()
for e in events:
evKeyNames.add(e['EventName'])
if b'ProtocolFocus' in evKeyNames:
stack = True
xmax = f2['x'].max()
ymax = f2['y'].max()
if xmax > ymax:
step = xmax/thumbSize
else:
step = ymax/thumbSize
im, edx, edy = histogram2d(f2['x'], f2['y'], [arange(0, xmax, step), arange(0, ymax, step)])
f1.close()
im = minimum(2*(255*im)/im.max(), 255).T
im = concatenate((im[:,:,newaxis], im[:,:,newaxis], im[:,:,newaxis]), 2)
if stack:
im[-10:, -10:, 0] = 180
if threeD:
im[-10:, -10:, 1] = 180
if split:
im[-10:-5, :10, 1] = 210
im[-5:, :10, 0] = 210
return im.astype('uint8')
def OpenFile(self, filename):
self.dataSources = []
if 'zm' in dir(self):
del self.zm
self.filter = None
self.mapping = None
self.colourFilter = None
self.filename = filename
self.selectedDataSource = inpFilt.H5RSource(filename)
self.dataSources.append(self.selectedDataSource)
self.mdh = MetaDataHandler.HDFMDHandler(self.selectedDataSource.h5f)
if 'Camera.ROIWidth' in self.mdh.getEntryNames():
x0 = 0
y0 = 0
x1 = self.mdh.getEntry(
'Camera.ROIWidth') * 1e3 * self.mdh.getEntry('voxelsize.x')
y1 = self.mdh.getEntry(
'Camera.ROIHeight') * 1e3 * self.mdh.getEntry('voxelsize.y')
if 'Splitter' in self.mdh.getEntry('Analysis.FitModule'):
y1 = y1 / 2
self.imageBounds = ImageBounds(x0, y0, x1, y1)
else:
self.imageBounds = ImageBounds.estimateFromSource(
self.selectedDataSource)
if 'fitResults_Ag' in self.selectedDataSource.keys():
#if we used the splitter set up a mapping so we can filter on total amplitude and ratio
#if not 'fitError_Ag' in self.selectedDataSource.keys():
if 'fitError_Ag' in self.selectedDataSource.keys():
self.selectedDataSource = inpFilt.MappingFilter(
self.selectedDataSource,
A='fitResults_Ag + fitResults_Ar',
gFrac='fitResults_Ag/(fitResults_Ag + fitResults_Ar)',
error_gFrac=
'sqrt((fitError_Ag/fitResults_Ag)**2 + (fitError_Ag**2 + fitError_Ar**2)/(fitResults_Ag + fitResults_Ar)**2)*fitResults_Ag/(fitResults_Ag + fitResults_Ar)'
)
sg = self.selectedDataSource['fitError_Ag']
sr = self.selectedDataSource['fitError_Ar']
g = self.selectedDataSource['fitResults_Ag']
r = self.selectedDataSource['fitResults_Ar']
I = self.selectedDataSource['A']
self.selectedDataSource.colNorm = np.sqrt(
2 * np.pi) * sg * sr / (2 * np.sqrt(sg**2 + sr**2) * I) * (
scipy.special.erf(
(sg**2 * r + sr**2 * (I - g)) /
(np.sqrt(2) * sg * sr * np.sqrt(sg**2 + sr**2))) -
scipy.special.erf(
(sg**2 * (r - I) - sr**2 * g) /
(np.sqrt(2) * sg * sr * np.sqrt(sg**2 + sr**2))))
self.selectedDataSource.setMapping('ColourNorm', '1.0*colNorm')
else:
self.selectedDataSource = inpFilt.MappingFilter(
self.selectedDataSource,
A='fitResults_Ag + fitResults_Ar',
gFrac='fitResults_Ag/(fitResults_Ag + fitResults_Ar)',
error_gFrac='0*x + 0.01')
self.selectedDataSource.setMapping('fitError_Ag',
'1*sqrt(fitResults_Ag/1)')
self.selectedDataSource.setMapping('fitError_Ar',
'1*sqrt(fitResults_Ar/1)')
sg = self.selectedDataSource['fitError_Ag']
sr = self.selectedDataSource['fitError_Ar']
g = self.selectedDataSource['fitResults_Ag']
r = self.selectedDataSource['fitResults_Ar']
I = self.selectedDataSource['A']
self.selectedDataSource.colNorm = np.sqrt(
2 * np.pi) * sg * sr / (2 * np.sqrt(sg**2 + sr**2) * I) * (
scipy.special.erf(
(sg**2 * r + sr**2 * (I - g)) /
(np.sqrt(2) * sg * sr * np.sqrt(sg**2 + sr**2))) -
scipy.special.erf(
(sg**2 * (r - I) - sr**2 * g) /
(np.sqrt(2) * sg * sr * np.sqrt(sg**2 + sr**2))))
self.selectedDataSource.setMapping('ColourNorm', '1.0*colNorm')
self.dataSources.append(self.selectedDataSource)
elif 'fitResults_sigxl' in self.selectedDataSource.keys():
self.selectedDataSource = inpFilt.MappingFilter(
self.selectedDataSource)
self.dataSources.append(self.selectedDataSource)
self.selectedDataSource.setMapping(
'sig', 'fitResults_sigxl + fitResults_sigyu')
self.selectedDataSource.setMapping(
'sig_d', 'fitResults_sigxl - fitResults_sigyu')
self.selectedDataSource.dsigd_dz = -30.
self.selectedDataSource.setMapping('fitResults_z0',
'dsigd_dz*sig_d')
else:
self.selectedDataSource = inpFilt.MappingFilter(
self.selectedDataSource)
self.dataSources.append(self.selectedDataSource)
if 'Events' in self.selectedDataSource.resultsSource.h5f.root:
self.events = self.selectedDataSource.resultsSource.h5f.root.Events[:]
evKeyNames = set()
for e in self.events:
evKeyNames.add(e['EventName'])
if b'ProtocolFocus' in evKeyNames:
self.zm = piecewiseMapping.GeneratePMFromEventList(
self.events, self.mdh, self.mdh.getEntry('StartTime'),
self.mdh.getEntry('Protocol.PiezoStartPos'))
self.z_focus = 1.e3 * self.zm(self.selectedDataSource['t'])
#self.elv.SetCharts([('Focus [um]', self.zm, 'ProtocolFocus'),])
self.selectedDataSource.z_focus = self.z_focus
self.selectedDataSource.setMapping('focus', 'z_focus')
if 'ScannerXPos' in evKeyNames:
x0 = 0
if 'Positioning.Stage_X' in self.mdh.getEntryNames():
x0 = self.mdh.getEntry('Positioning.Stage_X')
self.xm = piecewiseMapping.GeneratePMFromEventList(
self.elv.eventSource, self.mdh,
self.mdh.getEntry('StartTime'), x0, 'ScannerXPos', 0)
self.selectedDataSource.scan_x = 1.e3 * self.xm(
self.selectedDataSource['t'] - .01)
self.selectedDataSource.setMapping('ScannerX', 'scan_x')
self.selectedDataSource.setMapping('x', 'x + scan_x')
if 'ScannerYPos' in evKeyNames:
y0 = 0
if 'Positioning.Stage_Y' in self.mdh.getEntryNames():
y0 = self.mdh.getEntry('Positioning.Stage_Y')
self.ym = piecewiseMapping.GeneratePMFromEventList(
self.elv.eventSource, self.mdh,
self.mdh.getEntry('StartTime'), y0, 'ScannerYPos', 0)
self.selectedDataSource.scan_y = 1.e3 * self.ym(
self.selectedDataSource['t'] - .01)
self.selectedDataSource.setMapping('ScannerY', 'scan_y')
self.selectedDataSource.setMapping('y', 'y + scan_y')
if 'ScannerXPos' in evKeyNames or 'ScannerYPos' in evKeyNames:
self.imageBounds = ImageBounds.estimateFromSource(
self.selectedDataSource)
if not 'foreShort' in dir(self.selectedDataSource):
self.selectedDataSource.foreShort = 1.
if not 'focus' in self.selectedDataSource.mappings.keys():
self.selectedDataSource.focus = np.zeros(
self.selectedDataSource['x'].shape)
if 'fitResults_z0' in self.selectedDataSource.keys():
self.selectedDataSource.setMapping(
'z', 'fitResults_z0 + foreShort*focus')
else:
self.selectedDataSource.setMapping('z', 'foreShort*focus')
#if we've done a 3d fit
#print self.selectedDataSource.keys()
for k in self.filterKeys.keys():
if not k in self.selectedDataSource.keys():
self.filterKeys.pop(k)
#print self.filterKeys
self.RegenFilter()
if 'Sample.Labelling' in self.mdh.getEntryNames():
self.SpecFromMetadata()
